Methods and systems for correlating connections between users and links between articles

ABSTRACT

Methods and systems for correlating connections between users and links between articles to identify search and/or ad spamming are disclosed. Social networks can be used to identify connections between users for correlation with links between articles, which can be identified through searches of article contents and/or back tracing accesses to articles. One disclosed method comprises identifying first associations between a plurality of users in a network of associated users; identifying second associations between one or more users and one or more articles; identifying third associations between at least some of the articles or between some of the users and access to some of the articles; and determining at least one of the third associations is correlated with one or more of the first associations.

CROSS-REFERENCE TO RELATED APPLICATION

The application is a continuation of U.S. application Ser. No.11/026,681, filed Dec. 31, 2004, which is hereby incorporated byreference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to methods and systems foridentifying connections between users and links between articles. Forexample, embodiments of the present invention relate generally tomethods and systems for using social networks to identify connectionsbetween users, such as members in the social networks, and correlatingsuch connections with links between articles.

2. Background

Online advertising and search engines are ubiquitous on the Internet andWorld Wide Web. Online advertising in such forms as banner ads andpop-up ads include links that implore viewers to click on the ads and bedirected to a linked website for further information on the advertisedproducts or services. Advertisers entice websites to host ads by, forexample, paying a flat periodic fee or a set fee per each click through(i.e., a click on the ad to be directed to an advertiser's site). Suchfinancial structure for online advertising is susceptible to adspamming. For example, webmasters of ad-hosting websites can attempt toincrease their financial gains from advertisers by collaborating withone another to excessively click on ads hosted on their websites toincrease the number of click-throughs or give the appearance that suchads enjoy high traffic.

A search engine, such as the Google™ search engine, returns a result setin response to an input query submitted by a user. Such search enginemay use one or more various methods for performing informationretrieval. For example, one known method, described in an articleentitled “The Anatomy of a Large-Scale Hypertextual Search Engine,” bySergey Brin and Lawrence Page, assigns a degree of importance to adocument, such as a web page, based on the link structure of the webpage. The search engine ranks or sorts the individual articles ordocuments in the result set based on a variety of measures. For example,the search engine may rank the results based on a popularity score. Thesearch engine generally places the most popular results at the beginningof the result set.

Search methods that rely on link structures also can be susceptible tosearch spamming. For example, a plurality of webmasters or webadministrators can collaborate and link their websites with one anotherto increase the links to each website, or a single particular website,in order to increase the website's ranking and/or chance of appearancein a search result set returned by the search methods.

Social networking websites such as those hosted on Orkut™, Friendster™,Tribe™, or other websites, allow users to form social networks andbecome network members. Such networks on the social networking websitesallow members of each social network to communicate with each other andlist announcements associated with the social network. Generally, thesesocial networks do not communicate with search engines and onlineadvertisers, particularly those that rely on web link structures forsearching queries, in order to correlate connections between users andlinks between websites or web documents and identify ad and searchspammings.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide methods and systems forcorrelating connections between users and links between articles. In oneembodiment of the present invention, there is provided a methodcomprising: identifying first associations between a plurality of usersin a network of associated users; identifying second associationsbetween one or more users and one or more articles; identifying thirdassociations between at least some of the articles or between some ofthe users and access to some of the articles; and determining at leastone of the third associations is correlated with one or more of thefirst associations.

The aforementioned embodiment is mentioned not to limit or define theinvention, but to provide an example of embodiments of the invention toaid understanding thereof. Such an exemplary embodiment is discussed inthe Detailed Description, and further description of the invention isprovided there. Advantages offered by the various embodiments of thepresent invention may be further understood by examining thisspecification.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the present invention are illustrated by way ofexample in, and not limited to, the following figures:

FIG. 1 is a block diagram illustrating an exemplary environment in whichembodiments of the present invention may operate;

FIG. 2 depicts a diagram of a social network in accordance with anembodiment of the present invention;

FIG. 3 depicts a process flow for correlating connections between usersand links between articles in accordance with an embodiment of thepresent invention.

FIG. 4 depicts a process flow for correlating connections between usersand links between articles in accordance with another embodiment of thepresent invention; and

FIG. 5 depicts a process flow for correlating connections between usersand links between articles in accordance with still another embodimentof the present invention.

DETAILED DESCRIPTION Overview

As referred herein, search spamming includes any unauthorized or“unnatural” manipulation or exploitation of a search engine's searchingmethodology and/or search result sets in order to affect the listingand/or ranking of article identifiers in the search result sets.Likewise, as referred herein, ad spamming includes any unnaturalmanipulation of accesses to ads or the monitoring of ad access (e.g., anunnatural manipulation may include clicks on ads that are driven by somemotive other than genuine consumer interest in the content of the ad).

Also, as referred herein, a search result set comprises one or moreidentifiers of articles that are relevant to a search query. Articlesinclude, for example: word processor, spreadsheet, presentation, e-mail,instant messenger, database, and other client application programcontent files or groups of files; web pages of various formats (e.g.,HTML, XML, XHTML); portable document format (PDF) files; audio files;video files; or any other documents or groups of documents orinformation of any type whatsoever. Articles can be used as ads toadvertise products or services. An article identifier may be, forexample, a uniform resource locator (URL), a uniform resource identifier(URI), an Internet Protocol (IP) address, a file name, a link, an icon,a path for a local file, or anything else that identifies an article.

Further, as referred herein, each article has an article administratorthat is responsible for the design, implementation, and/or maintenanceof the article. Each article administrator can be identified by one ormore administrator identifiers, which may be, for example, a name of anarticle administrator, an e-mail address of an article administrator, oranything else that can identify an article administrator.

Embodiments of the present invention provide methods and systems foridentifying connections between users and links between articles andcorrelating the connections and links to determine the independence ofsuch links. In one embodiment, the method begins with a determination ofconnections between users based on personalization information obtainedfrom a social network to which the users belong. The connectionsinformation is then stored in a database. Next, desired articles arelocated from a network to test the independence of any links to and/orfrom such articles. The administrators of such articles, links to andfrom the articles, and the administrators of such links are alsoidentified and stored in an article index. Next, the connectionsinformation is retrieved from the database for correlation with theinformation stored in the article index to determine whether the linksto and from the articles are independent, e.g., such links are not basedon existing relationships/associations found between the users in thesocial network.

System Architecture

Various systems in accordance with the present invention may beconstructed. FIG. 1 is a block diagram illustrating an exemplary systemin which embodiments of the present invention can operate. The presentinvention may operate, and be embodied, in other systems as well.

Referring now to the drawings in which like numerals indicate likeelements throughout the several figures, FIG. 1 is a block diagramillustrating an exemplary system in accordance with an exemplaryembodiment of the present invention. The system 100 shown in FIG. 1includes multiple client devices 102 a-n with users 112 a-112 n incommunication with a search site 150 and a social network site 160 overa network 106. The search site 150 and the social network site 160 arealso in communication with each other directly (as shown by the dashedline) or through the network 106. The network 106 can be a wired orwireless network. Further, it can be a public network, e.g., theInternet, or a private data network, e.g., a local area network (LAN) ora wide area network (WAN). The search site is not required in someembodiments. Moreover, methods according to the present invention mayoperate within a single computer.

Each of the client devices 102 a-n includes a memory 108, which can be acomputer-readable medium (CRM), such as a random access memory (RAM),coupled to a processor 110. The processor 110 executescomputer-executable program instructions stored in the client device,such as memory 108, as program code. Such processor may include amicroprocessor, an ASIC, and state machines. Such processors include, ormay be in communication with, media, for example computer-readablemedia, which stores instructions that, when executed by the processor,cause the processor to perform the methods described herein. Moreover,the processor 110 can be any of a number of computer processors, such asprocessors from Intel Corporation of Santa Clara, Calif. and MotorolaCorporation of Schaumburg, Ill. Embodiments of computer-readable mediainclude, but are not limited to, an electronic, optical, magnetic, orother storage or transmission device capable of providing a processor,such as the processor 110 of client 102 a, with computer-readableinstructions. Other examples of suitable media include, but are notlimited to, a floppy disk, CD-ROM, DVD, magnetic disk, memory chip, ROM,RAM, an ASIC, a configured processor, all optical media, all magnetictape or other magnetic media, or any other medium from which a computerprocessor can read instructions. Also, various other forms ofcomputer-readable media may transmit or carry instructions to acomputer, including a router, switch, private or public network, orother transmission device or channel, both wired and wireless. Theinstructions may include code from any suitable computer-programminglanguage, including, for example, C, C++, C#, Visual Basic, Java,Python, Perl, and JavaScript.

Client devices 102 a-n can also include a number of external or internaldevices such as a mouse, a CD-ROM drive, a DVD drive, a keyboard, adisplay, or other input or output devices. Examples of client devices102 a-n are personal computers, digital assistants, personal digitalassistants (PDAs), cellular phones, mobile phones, smart phones, pagers,digital tablets, laptop computers, Internet appliances, and otherprocessor-based devices. In general, the client devices 102 a-n can beany type of processor-based platform that operates on any operatingsystem capable of supporting one or more client application programs.Client devices 102 a-n may operate on any operating system capable ofsupporting a browser or browser-enabled application, such as Microsoft®Windows® or Linux. The client devices 102 a-n shown include, forexample, personal computers executing a browser application program suchas Microsoft Corporation's Internet Explorer™, Netscape CommunicationCorporation's Netscape Navigator™, and Apple Computer, Inc.'s Safari™.

Through the client devices 102 a-n, users 112 a-n can communicate overthe network 106 with each other and with other sites, systems anddevices coupled to the network 106. As shown in FIG. 1, a search site150 and a social network site 160 are also coupled to the network 106.

The search site 150 shown includes a server device 152 executing asearch application program. Similar to the client devices 102 a-n, theserver device 152 shown includes a processor 154 coupled to a CRM 156.Server device 152, depicted as a single computer system, may beimplemented as a network of computer processors. Examples of the serverdevice 162 are servers, mainframe computers, networked computers, aprocessor-based device, and similar types of systems and devices. Theserver processor 154 can be any of a number of computer processors, suchas processors from Intel Corporation of Santa Clara, Calif. and MotorolaCorporation of Schaumburg, Ill.

Memory 156 contains a search application program, also known as a searchengine 158. The search engine 158 locates relevant information inresponse to a search query from one of the client devices 102 a-n, e.g.,the client device 102 a. The search engine 158 then provides a searchresult set to the client device 102 a via the network 106. The searchresult set comprises one or more identifiers of articles that arerelevant to the search query. Articles include, for example: wordprocessor, spreadsheet, presentation, e-mail, instant messenger,database, and other client application program content files or groupsof files; web pages or websites of various formats (e.g., HTML, XML,XHTML); portable document format (PDF) files; audio files; video files;or any other documents or groups of documents or information of any typewhatsoever. Articles can be used as ads to advertise products orservices. An article identifier may be, for example, a uniform resourcelocator (URL), a uniform resource identifier (URI), an Internet Protocol(IP) address, a file name, a link, an icon, a path for a local file, oranything else that identifies an article.

The social network site 160 shown includes a social network database 170and a server device 162 executing a social network engine applicationprogram. Similar to the client devices 102 a-n, the server device 162shown includes a processor 164 coupled to a CRM 166. The server device162 is in communication with a social network database 170. Serverdevice 162, depicted as a single computer system, may be implemented asa network of computer processors. Examples of the server device 162 areservers, mainframe computers, networked computers, a processor-baseddevice, and similar types of systems and devices. The server processor164 can be any of a number of suitable computer processors, such asprocessors from Intel Corporation of Santa Clara, Calif. and MotorolaCorporation of Schaumburg, Ill.

Memory 166 in the server device 162 contains a social network engineapplication program, also known as a social network engine 168. Thesocial network engine 168 allows users, such as user 112 a, to interactwith and participate in a social network. A social network can refer toa network connecting people or organization by a set of relationships,such as social relationships like friendship, co-working, or informationexchange. A social network can include profiles that can be associatedwith other profiles. Each profile may represent a member and a membercan be, for example, a person, an organization, a business, acorporation, a community, a fictitious person, or other entity. Eachprofile can contain entries, and each entry can include informationassociated with a profile. Examples of entries for a person profile caninclude information regarding relationship status, birth date, age,children, ethnicity, religion, political view, sense of humor, sexualorientation, fashion preferences, smoking habits, drinking habits, pets,hometown location, passions, sports, activities, favorite books, music,television, or movie preferences, favorite cuisines, email addresses,location information, IM name, phone number, address, skills, career, orany other information describing, identifying, or otherwise associatedwith a profile. Entries for a business profile can include marketsector, customer base, location, supplier information, net profits, networth, number of employees, stock performance, or other types ofinformation associated with the business profile.

Additionally, entries within a profile can include associations withother profiles. Associations between profiles within a social networkcan include, for example, friendships, business relationships,acquaintances, community or group associations, activity partnerassociations, common interest associations, common characteristicassociations, or any other suitable type of relationship connection(e.g., social relationship connection). Members can set up newassociations or join existing associations in the social network asdesired. For example, a member can set up a “Computer Science community”for those members who are interested or work in the computer sciencefield. Likewise, a member can join an existing “Baseball community” ifthe member is interested in baseball and/or sport in general.Associations between profiles can also have various levels. For example,friendship levels can include, for example, a “haven't met” level, an“acquaintance” level, a “friend” level, a “good friend” level, a “bestfriend” level, and other suitable levels.

A degree of separation based on associations between profiles can alsobe determined. For example, a degree of separation can be determinedbased on the fewest number of associations between two profiles. Thus,if profile A is a friend of profile B, and profile B is a friend ofprofile C, there can be a degree of separation of two between profiles Aand C. A degree of separation can be type specific or type neutral. Typespecific degrees of separation only count relationships of a certaintype. Thus, for example, in the case above where A is a friend of B, andB is a friend of C, there is a friendship degree separation of two, evenif A is directly associated with C by a business association, whichwould otherwise produce a degree of separation of 1.

Server device 162 of the social network site 160 also provides access tostorage elements, such as a social network storage element, in theexample shown in FIG. 1, a social network database 170. The socialnetwork database 170 can be used to store profiles of members in asocial network, communities within the social network as created by themember-network engine 168, and all identifiable connections betweenmembers in the social network. Data storage elements may include any oneor combination of methods for storing data, including withoutlimitation, arrays, hash tables, lists, and pairs. Other similar typesof data storage devices can be accessed by the server device 162. Thesocial network engine 168 can receive personalization informationassociated with each member in the social network from the socialnetwork database 170 and send personalization information of members tothe social network database 170 for storage. As referred herein, amember's personalization information includes any information that isassociated with the member, including any information: 1) contained inthe member's profile or other members' profiles; and/or 2) otherwisemaintained by the social network and is in any way associated with themember. Further, the social network engine 168 can derive allconnections between members in the social network from thepersonalization information of the members and store such connectionsinformation in the social network database 170, or another data storagedevice, as well. The member-network database 170 may be physicallyattached or otherwise in communication with the member-network engine168 by way of a network or other connection.

In operation, the social network engine 168 stores connectionsinformation of members in the social network database 170. Next, thesearch engine 158, or related devices, can perform a crawl of thenetwork 106 to identify: desired articles, administrators of thearticles, links to and/or from the articles, and administrators of suchlinks. Such information, once identified, can be stored in an articleindex for storage in memory 156 or any suitable data storage device. Thesearch engine can subsequently retrieve the information in the articleindex and correlate with the connections information stored in thesocial network database 170 to fine tune future searches and/or identifyad spamming.

It should be noted that the present invention may include systems havingdifferent architecture than that which is shown in FIG. 1. For example,in some systems according to the present invention, server device 162may include a single physical or logical server. The system 100 shown inFIG. 1 is merely exemplary, and is used to help explain the socialnetworks and methods illustrated in subsequent figures.

Exemplary Social network

FIG. 2 shows a diagram of a social network 200 according to oneembodiment of the present invention. According to the embodimentillustrated in FIG. 2, the social network 200 is illustrated with agraph comprising vertices 202, 204, 206, 208, 210, 212, and 214 andedges 218, 220, 222, 224, 226, 228, 230, 232, and 234. The vertices 202,204, 206, 208, 210, 212, and 214 comprise profiles A, B, C, D, E, F, andG, respectively. Each profile can represent a member profile of a memberof the social network 200. The exemplary network 200 shown in FIG. 2 hasseven members. Considerably more members can be part of the socialnetwork 200. A member can be an entity such as, for example, a person,an organization, a business, a corporation, a community, a fictitiousperson, or other suitable entity.

Each member profile can contain entries, and each entry can compriseinformation associated with a profile. For example, a person's memberprofile can contain: personal information, such as relationship status,birth date, age, children, ethnicity, religion, political view, sense ofhumor, sexual orientation, fashion preferences, smoking habits, drinkinghabits, pets, hometown location, passions, sports, activities, favoritebooks or music, television or movie preferences, and favorite cuisines;contact information, such as email addresses, location information,instant messenger name, telephone numbers, and address; professionalinformation, such as job title, employer, and skills; educationalinformation, such as schools attended and degrees obtained, and anyother suitable information describing, identifying, or otherwiseassociated with a person. A business' member profile can, for example,contain a description of the business, and information about its marketsector, customer base, location, suppliers, net profits, net worth,number of employees, stock performance, contact information, and othertypes of suitable information associated with the business.

A member profile can also contain rating information associated with themember. For example, the member can be rated or scored by other membersof the social network 200 in specific categories, such as humor,intelligence, fashion, trustworthiness, sexiness, and coolness. Amember's category ratings can be contained in the member's profile. Inone embodiment of the social network, a member can have fans. Fans canbe other members who have indicated that they are “fans” of the member.Rating information can also include the number of fans of a member andidentifiers of the fans. Rating information can also include the rate atwhich a member accumulated ratings or fans and how recently the memberhas been rated or acquired fans.

A member profile can also contain membership information associated withthe member. Membership information can include information about amember's login patterns to the social network, such as the frequencythat the member logs in to the social network and the member's mostrecent login to the social network. Membership information can alsoinclude information about the rate and frequency that a member profilegains associations to other member profiles. In a social network thatcomprises advertising or sponsorship, a member profile may containconsumer information. Consumer information may include the frequency,patterns, types, or number of purchases the member makes, informationabout which web sites the member has accessed or used, or informationabout which advertisers or sponsors the member has accessed, patronized,or used.

A member profile may comprise data stored in memory. The profile, inaddition to comprising data about the member, can also comprise datarelating to others. For example, a member profile can contain anidentification of associations or virtual links with other memberprofiles. In one embodiment, a member profile includes an identificationof association(s) to which the member belongs. For example, a memberprofile can indicate that the member belongs to the Computer Sciencecommunity. In another embodiment, a member profile may comprise ahyperlink associated with another member's profile. In one suchassociation, the other member's profile may contain a reciprocalhyperlink associated with the first member's profile. A member's profilemay also contain information excerpted from another associated member'sprofile, such as a thumbnail image of the associated member, his or herage, marital status, and location, as well as an indication of thenumber of members with which the associated member is associated. In oneembodiment, a member's profile may comprise a list of other members'profiles with which the member wishes to be associated.

An association may be designated manually or automatically. For example,a member may designate associated members manually by selecting otherprofiles and indicating an association that can be recorded in themember's profile. Also, an association between two profiles may comprisean association automatically generated in response to a predeterminednumber of common entries, aspects, or elements in the two members'profiles. In one embodiment, a member profile may be associated with allof the other member profiles comprising a predetermined number orpercentage of common entries, such as interests, hobbies, likes,dislikes, employers and/or habits.

Associations between profiles within a social network can be of a singletype or can be multiple types and can include, for example, friendshipassociations, business associations, family associations, communityassociations, school associations, or any other suitable type of linkbetween profiles. Associations can further be weighted to represent thestrength of the association. For example, a friendship association canbe weighted more than a school association. Each type of association canhave various levels with different weights associated with each level.For example, a friendship association can be classified according towhich of a plurality of friendship association levels it belongs to. Inone embodiment, a friendship association may be assigned a level by themember from a list of levels comprising: a best friend, a good friend, aregular friend, an acquaintance, and a friend the member has not met.

In FIG. 2, the edges 218, 220, 222, 224, 226, 228, 230, 232, and 234shown comprise associations between profiles. According to theembodiment shown in FIG. 2, the social network 200 comprises a pluralityof differing types of associations represented by edges 218, 220, 222,224, 226, 228, 230, 232, and 234. The types of associations shown inFIG. 2 for illustration purposes are business associations, activitypartner associations, friendship associations, community associations,and common characteristic associations. Common characteristicassociations may include, for example, associations based on somecharacteristic, such as attending the same high school or being from thesame hometown, and can indicate a lower level of significance thananother type of association, such as a friendship association.

Referring to FIG. 2, edge 220 and edge 222 each comprise an associationbetween profile A at vertex 202 and profile D at vertex 208. The edge220 represents a business association, and the edge 222 represents afriendship association. Profile A is also associated with profile E by acommon characteristic association comprising edge 218. The associationbetween profile A and profile E may be more attenuated than theassociation between profile A and D, but the association can still berepresented by the social network depicted in FIG. 2.

Each member represented by the profiles A, B, C, D, E, F, and Gcomprising the vertices 202, 204, 206, 208, 210, 212, and 214,respectively, for purposes of illustration, comprises a person. Othertypes of members can be in social network 200. For example, communities,special interest groups, organizations, political parties, universities,and legal persons, such as corporations and business partnerships may bemembers of the social network 200. The associations 218, 220, 222, 224,226, 228, 230, 232, and 234 illustrated in FIG. 2 comprisebi-directional associations. An association between two profiles maycomprise a bi-directional association when both parties to theassociation are associated with each other. For example, in FIG. 2,profile A is associated with profile D, and profile D is also associatedwith profile A. In one embodiment, profiles A and D will not bebi-directionally associated with each other until both profiles consentto such an association. For example, profile A may invite profile D tobe associated therewith, and the bi-directional association occurs uponprofile D's acceptance of such invitation. The invitation, for example,may include sending an email or other message to profile D indicatingthat profile A has requested an association with profile D.

Other embodiments of the present invention may comprise directedassociations or other types of associations. Directed associations canassociate a first profile with a second profile while not requiring thesecond profile to be associated with the first profile. For example,profile A can be associated by a friendship association with profile B,and profile B can be unassociated with profile A, or profile B can beassociated with profile A through a different type of association, suchas a business association. Thus a display of profile A's friends wouldinclude profile B, but a display of profile B's friends would notinclude profile A.

Within a social network, a degree of separation can be determined forassociated profiles. In one embodiment, a degree of separation betweentwo profiles can be determined by the fewest number of edges of acertain type separating the associated profiles. In another embodiment,a type-specific degree of separation may be determined. A type-specificdegree of separation comprises a degree of separation determined basedon one particular type of association. For example, a profile A has afriend association degree of separation of two from profile E. Thefewest number of friendship associations between profile A and profile Eis two—the friendship association comprising edge 222 between profiles Aand D and the friendship association comprising edge 234 betweenprofiles D and E. Thus, for the associated profiles A and E, the degreeof friendship separation, determined according to one aspect of oneembodiment of the present invention, is two.

Another type-specific degree of separation can also be determined forprofiles A and E. For example, a common characteristic degree ofseparation can be determined by determining the fewest number of commoncharacteristic associations separating profile A and profile E.According to the embodiment depicted in FIG. 2, there is one commoncharacteristic association, comprising edge 218, separating profiles Aand E. Thus, the common characteristic association degree of separation,according to the embodiment depicted in FIG. 2, is one. The commoncharacteristic in this example, can be that profile A attended the samehigh school as profile E. A common characteristic association may beselected by profiles A and E to represent that they are associated insome fashion, but to not create a close association such as with afriendship association.

According to other aspects of certain embodiments of the presentinvention, the degree of separation may be determined by use of aweighting factor assigned to each association. For example, closefriendships can be weighted higher than more distant friendships.According to certain aspects of embodiments using a weighting factor, ahigher weighting factor for an association can reduce the degree ofseparation between profiles and lower weighting factors can increase thedegree of separation. This can be accomplished, for example, byestablishing an inverse relationship between each associations and acorresponding weighting factor prior to summing the associations. Thus,highly weighted associations would contribute less to the resulting sumthan lower weighted associations.

Process

Various methods or processes in accordance with the present inventionmay be constructed. For example, in one embodiment, the method beginswith the social network engine 168 determining connections informationbetween members in a social network and storing the connectionsinformation in the memory 156 or any suitable memory device or database.Next, the search engine 158 locates the desired articles for review,e.g., for search or ad spamming, and identifies the administratorresponsible for such articles. The search engine 158 also identifieslinks to or from the desired articles based on a content search of sucharticles or a back tracing or tracking of accesses to such articles. Thesearch engine 158 further identifies the administrator of each link sothat it can correlate such information with the connections informationand determine the independence of each link. In an alternativeembodiment, a search site or search engine is not required. Instead, acrawl or any other method can be used to retrieve the contents ofdesired web sites to locate desired articles for review, links to andfrom those desired articles, and administrators of such links.

FIGS. 3-5 provide exemplary methods that identify connections betweenusers and links between articles and correlate the users' connectionswith the articles' links. The exemplary methods are provided by way ofexamples, as there are a variety of ways to carry out the methodsaccording to the present invention. The methods shown in FIGS. 3-5 canbe executed or otherwise performed by one or a combination of varioussystems. The methods in FIGS. 3-5 are described below as carried out bythe system 100 shown in FIG. 1 by way of example, and various elementsof the system 100 are referenced in explaining the example methods ofFIGS. 3-5.

Referring now to the method depicted in FIG. 3, the method begins at 310with the social network engine 168, or related devices, gatheringpersonalization information of each member in the social network fromthe social network database 170 to determine connections between themembers, including types of association, association levels andassociation weights given.

At 320, the social network engine 168 stores the connections informationin the social network database 170, or another data storage device, atthe social network site 160 or any other desired site. The connectionsinformation for each connection includes: 1) information identifying themembers that are connected to or associated with one another; 2) thetype or types of connection/association between the members, includingthe degree of separation between the members and any virtual linksbetween the members; and 3) the level and weight assigned to each typeof association.

Each member can be identified by the member's name, e-mail address,telephone number, address, and/or any other information that canuniquely identify the member and is maintained by the social network. Asmentioned earlier in the exemplary social network description, the typesof connection or association include: friendships, businessrelationships, family associations, acquaintances, community or groupassociations, activity partner associations, common interestassociations, common characteristic associations, or any other suitabletype of relationship connections (e.g., social relationship connection).For example, referring to the social network 200 in FIG. 2, theconnections information determined and stored in the social networkdatabase 170 for the connection/association between profile A (vertex202) and profile B (vertex 204) would include: 1) informationidentifying a member A to which the profile A belongs and a member B towhich the profile B belongs; 2) the business association (edge 224), thefriendship association (edge 226), and the activity partner association(edge 228) between members A and B; and 3) the level and weight assignedto each of the business, friendship, and activity associations. As afurther example, member A is at a best-friend level with member B, but agood-friend level (edge 232) with member C (represented by profile C atvertex 206); therefore, more weight is given to the friendshipassociation between members A and B than the friendship associationbetween members A and C.

Likewise, the connections information determined and stored in thesocial network database 170 for the connection/association betweenprofile A (vertex 202) and profile G (vertex 214) would include: 1)information identifying a member A to which the profile A belongs and amember G to which the profile G belongs; 2) the two-degree-of-separationfriendship association (edges 232 and 236) between members A and G; and3) a lower weight assigned to such friendship association, relative tothe weight assigned to the friendship association (edge 232) betweenmembers A and C.

Referring back to the method shown in FIG. 3, at 330, the search engine158, or related devices, performs a crawl of the network 106 to locateone or more desired articles stored at other devices or systems coupledto the network 106. Alternatively, articles may be identified without acrawl—e.g., content sites may provide a list of article; all articles ona particular site can be associated with the owner; or articleidentifiers may be observed via a proxy log, which may contain a list ofaccesses by users to articles (e.g., users A and B are identified asbeing related based on which sites they visit, and exclude accesses toads by B on a site owned by A, without ever crawling the site owned byA. The desired articles to be located are based on requests to correlatesuch articles with connections between members in a social network. Forexample, an administrator of the search site 150 or search engine 158may request a review of a plurality of websites X, Y, Z to down-weightor up-weight links to and from such websites for future searches.

Next, at 340, also from the network crawl, the search engine 158identifies the webmaster, web administrator, the owner, or any otherentity responsible for the design, implementation, operation, and/ormaintenance of each article in order to associate an administratoridentifier with each of desired articles that have been located. Thearticle administrator can be identified through a search of the contentof each article for any information that can identify the administrator.For example, an article such as a website can be searched for contact orauthorship information (e.g., name, e-mail address, postal addressand/or telephone number). In another example, an article administratorcan be identified from a domain name registration service that providesregistration for the particular article or the web site that hosts thearticle. In another example, an article administrator may be inferredfrom access patterns; for example, it can be inferred that a user islikely to be the administrator of a site based on the frequency oruniformity of accesses to articles on the site (e.g., other users mayaccess specific pages while the administrator may access more of thepages when testing their site, or the administrator may regularly checkcertain pages). In another example, an article administrator may beidentified by analyzing a news group of bulletin board postings wherethe administrator refers to the article or an associated entity such asa web site.

At 350, also from the network crawl, the search engine 158 searches thecontent of each desired article to determine if it provides links toother articles. For example, the content of a first desired article(hereinafter, “linking article”) is searched to determine if it provideslinks to one or more other articles (hereinafter, “destinationarticles”). Furthermore, the search engine 158 can search the contentsof the destination articles to determine whether they provide links backto the first linking article. It should be noted that the destinationarticles can include the desired articles located at 330 or otherarticles.

At 360, assuming that the linking article provides links to one or moredestination articles, the search engine 158 also obtains administratoridentifiers for such destination articles in the same manner asexplained at 340.

At 370, the search engine 158 indexes the desired articles (i.e.,linking articles) by their article identifiers, administratoridentifiers, links to other destination articles, the administratoridentifiers for the destination articles, and any links from thedestination articles back to the linking articles in an article indexfor storage in memory 156 or any suitable data storage device. Thearticle index can be in a spreadsheet or table format, or any otherformat, that allows a search for an article, and its associatedadministrator through an administrator identifier. In other embodiments,the desired articles can be processed in some order, without beingindexed, for storage in and subsequent retrieval from memory 156 or anysuitable data storage device. Further, the processing of the desiredarticles may occur anywhere at the search site 150 or any otherlocation(s), and on one or more machines, servers, or systems.

At 380, the search engine 158 communicates with the social networkengine 168 to: 1) look up the stored connections information in thesocial network database 170; 2) correlate the connections informationwith the information found in the article index to determine types ofassociation, including association levels and weights given, betweenadministrators of linking articles and those of destination articles;and 3) assign searching weights or importance of each links betweenarticles for future searches at the search site 150 with the searchengine 158. For example, referring back to the social network 200 inFIG. 2, a member A with the associated profile A (vertex 202) maymaintain a website A that includes links to websites B and C maintainedby member B (with associated profile B at vertex 204) and member C (withassociated profile B at vertex 206), respectively. As described earlier,members A and B are at a best-friend level, whereas members A and C areat a good-friend level. Thus, more association weight is given to thefriendship association between members A and B than the friendshipassociation between members A and C. As a result, member A is determinedto have a more favorable bias towards member B and associated website B,and any link from website A to website B may not be consideredindependent and thus accorded a lower searching weight or importance forsearching purposes. On the contrary, because of a lower-weightassociation between members A and C, and therefore less favorable bias,any link from website C to website A may be considered independent andthus accorded a higher weight or importance for searching purposes. Theamount of searching weight or importance assigned to each type, level,and weight of association merely depends on the programming of thesearch engine 158, as based on the desire of the search site 150.

Referring now to the method depicted in FIG. 4, similar to the methoddepicted in FIG. 3, this method also begins at 410 with the socialnetwork engine 168, or related devices, gathering personalizationinformation of each member in the social network from the social networkdatabase 170 to determine connections between the members, includingtypes of association, association levels and association weights given.

At 420, the social network engine 168 also stores the connectionsinformation in the social network database 170, or another data storagedevice, at the social network site 160 or any other desired site. Theconnections information for each connection includes: 1) informationidentifying the members that are connected to or associated with oneanother; 2) the type or types of connection/association between themembers, including the degree of separation between the members and anyvirtual links between the members; and 3) the level and weight assignedto each type of association.

As mentioned earlier, each member can be identified by the member'sname, e-mail address, telephone number, address, and/or any otherinformation that can uniquely identify the member and is maintained bythe social network. As mentioned earlier in the exemplary social networkdescription, the types of connection or association include:friendships, business relationships, family associations, acquaintances,community or group associations, activity partner associations, commoninterest associations, common characteristic associations, or any othersuitable type of relationship connections (e.g., social relationshipconnection). Examples of the connection/association are as providedearlier with reference to 320 in FIG. 3.

At 430, the search engine 158, or related devices, also performs a crawlof the network 106 to locate one or more desired articles stored atother devices or systems coupled to the network 106. The desiredarticles to be located are based on requests to correlate such articleswith connections between members in a social network. For example, anadministrator of the search site 150 or search engine 158 may request areview of a plurality of websites X, Y, Z to down-weigh or up-weighlinks to and from such websites for future searches.

Next, at 440, also from the network crawl, the search engine 158identifies the webmaster, web administrator, or any other entityresponsible for the design, implementation, and/or maintenance of eacharticle in order to associate an administrator identifier with each ofdesired articles that have been located. The article administrator canbe identified through a search of the content of each article for anyinformation that can identify the administrator. For example, an articlesuch as a website can be searched for contact or authorship information(e.g., name, e-mail address, postal address and/or telephone number). Inanother example, an article administrator can be identified from adomain name registration service that provides registration for theparticular article or the web site that hosts the article. In anotherexample, an article administrator may be inferred from access patterns;for example, it can be inferred that a user is likely to be theadministrator of a site based on the frequency or uniformity of accessesto articles on the site (e.g., other users may access specific pageswhile the administrator may access more of the pages when testing theirsite, or the administrator may regularly check certain pages). Inanother example, an article administrator may be identified by analyzinga news group of bulletin board postings where the administrator refersto the article or an associated entity such as a web site.

At 450, if authorized or provided by the administrators of each desiredarticle (or any other entity with authority over such article), thesearch engine 158 can obtain information regarding access to eachdesired article and identify the points of access to the article. Forexample, when an access is made to the article, such access can betraced back from the article to the point of access to identify thecorresponding IP address and the user associated with such IP address.In another example, the point of access has embedded cookie(s) foridentification, and the access can be traced back to retrieve theembedded cookie(s) to identify the user accessing the article. Inanother example, the user accessing an article may be known by theservice provider, for example, a user may have an account with an ISPand when they connect to the network the ISP knows the user and whicharticles they access. Further, the search engine 158 can search thedesired articles to determine whether it also provides links back to thepoints of access, for example, when such points of access are alsoarticles (hereinafter, “accessing articles”). It should be noted thatthe accessing articles can include the desired articles located at 430or other articles.

At 460, assuming that there are one or more points of access identified,the search engine 158 also obtains administrator/user identifiers atsuch points of access in the same manner described earlier at 340.

At 470, the search engine 158 indexes the desired articles (i.e.,linking articles) by their article identifiers, administratoridentifiers, their points of access, the administrator identifiers orusers at the points of access, and any links from each desired articleback to its accessing articles in an article index for storage in memory156 or any suitable data storage device. The article index can be in aspreadsheet or table format, or any other format, that allows a searchfor an article, and its associated administrator through anadministrator identifier.

At 480, the search engine 158 communicates with the social networkengine 168 to: 1) look up the stored connections information in thesocial network database 170; 2) correlate the connections informationwith the information found in the article index to determine types ofassociation, including association levels and weights given, betweenadministrators of desired articles and those administrators/users at thepoints of access; and 3) assign searching weights or importance of eachlink between articles for future searches at the search site 150 withthe search engine 158. For example, referring back to the social network200 in FIG. 2, a member A with the associated profile A may maintain awebsite A that is accessed by B and C from their own respective websites(e.g., accessing articles) and/or their own connections to the network106 (e.g., IP addresses). As described earlier, members A and B are at abest-friend level, whereas members A and C are at a good-friend level.Thus, more association weight is given to the friendship associationbetween members A and B than the friendship association between membersA and B. As a result, member B is determined to have a more favorablebias towards member A and associated website A, and any link from awebsite or IP address of B to website A may not be consideredindependent and thus accorded a lower searching weight or importance forsearching purposes. On the contrary, because of a lower-weightassociation between members A and C, and therefore less favorablebiased, any link from website or IP address of C to website A may beconsidered independent and thus accorded a higher weight or importancefor searching purposes. The amount of searching weight or importanceassigned to each type, level, and weight of association merely dependson the programming of the search engine 158, as based on the desire ofthe search site 150.

Referring now to the method depicted in FIG. 5, this method begins at510 with the social network engine 168, or related devices, gatheringpersonalization information of each member in the social network fromthe social network database 170 to determine connections between themembers, including types of association, association levels andassociation weights given.

At 520, the social network engine 168 stores the connections informationin the social network database 170, or another data storage device, atthe social network site 160 or any other desired site. The connectionsinformation for each connection includes: 1) information identifying themembers that are connected to or associated with one another; 2) thetype or types of connection/association between the members, includingthe degree of separation between the members and any virtual linksbetween the members; and 3) the level and weight assigned to each typeof association.

As mentioned earlier, each member can be identified by the member'sname, e-mail address, telephone number, address, and/or any otherinformation that can uniquely identify the member and is maintained bythe social network. As mentioned earlier in the exemplary social networkdescription, the types of connection or association include:friendships, business relationships, family associations, acquaintances,community or group associations, activity partner associations, commoninterest associations, common characteristic associations, or any othersuitable type of relationship connections (e.g., social relationshipconnection). Examples of the connection/association are as providedearlier with reference to 320 in FIG. 3

At 530, the search engine 158, or related devices, performs a crawl ofthe network 106 to locate one or more desired articles that are ads onthe network 106. The desired ads to be located are based on requests tocorrelate such ads with connections between members in a social network.The desired ads, which can be all ads, can be located as identified byadvertisers of such ads or others who wish to detect spamming of theads. For example, an advertiser of an ad hosted at a website issuspicious of the click-through rate (CTR) it receives from the ad andwishes to verify that the CTR is independent from the hosted website.

Next, at 540, also from the network crawl, the search engine 158identifies the advertiser, or any other entity responsible for thedesign, implementation, and/or maintenance of each ad, in order toassociate an administrator identifier with each of desired ads that havebeen located. The advertiser can be identified from information providedby the advertiser itself. Alternatively, the advertiser can beidentified from a search of the ad content for any information that canidentify the ad administrator, the content of the article to which thead is linked, and/or a domain name registration service that providesregistration of the ad and/or the ad-linked-to article.

At 550, as authorized or provided by the ad administrators of eachdesired article (or any other entity with authority over such article),the search engine 158 can obtain information regarding access to eachdesired ad and identify the points of access to the ad. For example,when an access is made to the ad, such access can be traced back fromthe ad to the point of access to identify the corresponding IP addressand the user associated with such IP address. In another example, thepoint of access has embedded cookie(s) for identification, and theaccess can be traced back to retrieve the embedded cookie(s) to identifythe user accessing the ad (or an ISP may know the user as mentionedearlier). Further, the search engine 158 can search the hosted article(e.g., hosted website) of the ad to determine whether such hostedarticle also provides links back to the points of access, for example,when such points of access are also articles (hereinafter, “accessingarticles”).

At 560, assuming that there are one or more points of access identified,the search engine 158 also obtains administrator identifiers or users atsuch points of access as described above.

At 570, the search engine 158 indexes the desired ads by their articleidentifiers, administrator identifiers, their points of access, theadministrator identifiers or users at the points of access, and any linkfrom each hosted article of each ad back to a point of access in an adindex for storage in memory 156 or any suitable data storage device.Similar to the article index, the ad index can be in a spreadsheet ortable format, or any other format, that allows a search for an article,and its associated administrator through an administrator identifier.

At 580, the search engine 158 communicates with the social networkengine 168 to: 1) look up the stored connections information in thesocial network database 170; 2) correlate the connections informationwith the information found in the ad index to determine types ofassociation, including association levels and weights given, betweenadministrators of desired articles and those administrators/users at thepoints of access; and 3) determine whether ad spamming exists betweenmembers based on, e.g., click through rates from associated members. Forexample, referring back to the social network 200 in FIG. 2, a member Awith the associated profile A may maintain an ad at website A that isaccessed by B and F from their own respective websites (e.g., accessingarticles) and/or their own connections to the network 106 (e.g., IPaddresses). As described earlier, members A and B are at a best-friendlevel, whereas members A and F are merely associated through threedegrees of friendship. Thus, more association weight is given to thefriendship association between members A and B. Accordingly, member B isdetermined to be more favorably biased toward member A, associatedwebsite A, and any ad hosted by website A. Thus, any click through of anad hosted by website A from a website or IP address of B may beconsidered as ad spamming and disregarded. On the contrary, lessassociation weight is given to the friendship association betweenmembers A and F. Accordingly, member F is determined to not exhibit anyfavorable bias toward member A. Thus, any click through of an ad hostedby website A from a website or IP address of F may be consideredindependent and counted as a legitimate click-through. The amount ofweight or importance assigned to each type, level, and weight ofassociation for a determination of ad spamming merely depends on theprogramming of the search engine 158, as based on the desire of thesearch site 150 and/or the entity seeking to identify ad spamming.

GENERAL

Although the invention has been described with reference to theseembodiments, other embodiments could be made by those in the art toachieve the same or similar results. Variations and modifications of thepresent invention will be apparent to one skilled in the art based onthe present disclosure, and the present invention encompasses all suchmodifications and equivalents.

The invention claimed is:
 1. A computer-implemented method comprising: identifying, with one or more processors, a first connection between a first user and a second user in a social network and a second connection between the first user and a third user in the social network, each of the first and second connections including an association type and a degree of separation that identifies a fewest number of common characteristic associations separating the users; applying, with the one or more processors, a first association weight to the first connection that is higher than a second association weight applied to the second connection based on at least one of the association type and the degree of separation; identifying, with the one or more processors, the first user as a first administrator of a first article, the first article comprising a first link to a second article; identifying, with the one or more processors, that the second user and the third user access the first article; identifying, with the one or more processors, that the second user is a second administrator of the second article; and determining, with the one or more processors, a first importance score for the first link based on the first association weight, wherein the higher association weight indicates that the first connection is more biased.
 2. The method of claim 1, further comprising: searching the content of the first article to identify that the first article includes a second link to a third article; identifying that the third user is a third administrator of the third article; and determining a second importance score for the second link based on the second association weight, wherein the first importance score is lower than the second importance score because of the higher association weight between the first and the second user.
 3. The method of claim 1, wherein the second article is an advertisement and further comprising: receiving a click-through of the advertisement from the second user; and discounting the click-through based on the bias between the first user and the second user.
 4. The method of claim 1, wherein the association type comprises at least one of a friendship association, a business association, an activity partner association, a community association, a common characteristic association, a common interest association and a family association.
 5. The method of claim 4, wherein the association type is a friendship association and the first and second association weights are also based on an association level that comprises at least one of a have not met level, an acquaintance level, a friend level, a good friend level and a best friend level.
 6. The method of claim 1, further comprising locating the article based at least in part on a request from the first user.
 7. The method of claim 1, further comprising creating an article index for the article, the administrator, the first connection and the second connection.
 8. A system comprising: one or more processors; a social network engine stored in a memory and executable by the one or more processors, the social network engine for identifying a first connection between a first user and a second user in a social network and a second connection between the first user and a third user in the social network, each of the first and second connection including an association type and a degree of separation that identifies a fewest number of common characteristic associations separating the users and for applying a first association weight to the first connection that is higher than a second association weight applied to the second connection based on at least one of the association type and the degree of separation; and a search engine executable by the one or more processors, the search engine for identifying the first user as a first administrator of a first article, the first article comprising a first link to a second article, identifying that the second user and the third user access the first article, identifying that the second user is a second administrator of the second article and determining a first importance score for the first link based on the first association weight, wherein the higher association weight indicates that the first connection is more biased.
 9. The system of claim 8, wherein the search engine: searches the content of the first article to identify that the first article includes a second link to a third article; identifies that the third user is the third administrator of the third article; and determines a second importance score for the second link based on the second association weight, wherein the first importance score is lower than the second importance score because of the higher association weight between the first and the second user.
 10. The system of claim 8, wherein the second article is an advertisement and the search engine: receives a click-through of the advertisement from the second user; and discounts the click-through based on the bias between the first user and the second user.
 11. The system of claim 8, wherein the association type comprises at least one of a friendship association, a business association, an activity partner association, a community association, a common characteristic association, a common interest association and a family association.
 12. The system of claim 11, wherein the association type is a friendship association and the first and second association weights are also based on an association level that comprises at least one of a have not met level, an acquaintance level, a friend level, a good friend level and a best friend level.
 13. The system of claim 8, wherein the search engine locates the article based at least in part on a request from the first user.
 14. A non-transitory computer program product comprising a computer useable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: identify a first connection between a first user and a second user in a social network and a second connection between the first user and a third user in the social network, each of the first and second connection including an association type and a degree of separation that identifies a fewest number of common characteristic associations separating the users; apply a first association weight to the first connection that is higher than a second association weight applied to the second connection based on at least one of the association type and the degree of separation; identify the first user as a first administrator of a first article, the first article comprising a first link to a second article; identify that the second user and the third user access the first article; identify that the second user is a second administrator of the second article; and determine a first importance score for the first link based on the first association weight, wherein the higher association weight indicates that the first connection is more biased.
 15. The non-transitory computer program product of claim 14, wherein the computer readable program when executed on the computer further causes the computer to: search the content of the first article to identify that the first article includes a second link to a third article; identify that the third user is the administrator of the third article; and determine a second importance score for the second link based on the second association weight, wherein the first importance score is lower than the second importance score because of the higher association weight between the first and the second user.
 16. The non-transitory computer program product of claim 14, wherein the second article is an advertisement and the computer readable program when executed on the computer further causes the computer to: receive a click-through of the advertisement from the second user; and discount the click-through based on the bias between the first user and the second user.
 17. The non-transitory computer program product of claim 14, wherein the association type comprises at least one of a friendship association, a business association, an activity partner association, a community association, a common characteristic association, a common interest association and a family association.
 18. The non-transitory computer program product of claim 17, wherein the association type is a friendship association and the first and second association weights are also based on an association level that comprises at least one of a have not met level, an acquaintance level, a friend level, a good friend level and a best friend level.
 19. The non-transitory computer program product of claim 14, wherein the computer readable program when executed on the computer further causes the computer to locate the article based at least in part on a request from the first user.
 20. The non-transitory computer program product of claim 14, wherein the computer readable program when executed on the computer further causes the computer to create an article index for the article, the administrator, the first connection and the second connection. 